Figure 1.
Number of identified groups and MST total distance in each time window. Source: own construction based on OWID data
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Explaining the definition of wholesale access prices in the Portuguese telecommunications industry
January
2022, 9(1): 75-96.
doi: 10.3934/jdg.2021026
Analysis of communities of countries with similar dynamics of the COVID-19 pandemic evolution
| 1. | Research Group in Economic Dynamics, Faculty of Economics and Administration, Universidad de la República, Montevideo, Uruguay |
| 2. | Institute for Latin American Studies and School of Business & Economics, Freie Universität Berlin, Berlin, Germany |
This work addresses the spread of the coronavirus through a non-parametric approach, with the aim of identifying communities of countries based on how similar their evolution of the disease is. The analysis focuses on the number of daily new COVID-19 cases per ten thousand people during a period covering at least 250 days after the confirmation of the tenth case. Dynamic analysis is performed by constructing Minimal Spanning Trees (MST) and identifying groups of similarity in contagions evolution in 95 time windows of a 150-day amplitude that moves one day at a time. The intensity measure considered was the number of times countries belonged to a similar performance group in constructed time windows. Groups' composition is not stable, indicating that the COVID-19 evolution needs to be treated as a dynamic problem in the context of complex systems. Three communities were identified by applying the Louvain algorithm. Identified communities analysis according to each country's socioeconomic characteristics and variables related to the disease sheds light on whether there is any suggested course of action. Even when strong testing and tracing cases policies may be related with a more stable dynamic of the disease, results indicate that communities are conformed by countries with diverse characteristics. The best option to counteract the harmful effects of a pandemic may be having strong health systems in place, with contingent capacity to deal with unforeseen events and available resources capable of a rapid expansion of its capacity.
Mathematics Subject Classification:
Primary: 62H30, 91C20.
Citation:
Emiliano Alvarez, Juan Gabriel Brida, Lucía Rosich, Erick Limas. Analysis of communities of countries with similar dynamics of the COVID-19 pandemic evolution. Journal of Dynamics and Games,
2022, 9
(1)
: 75-96.
doi: 10.3934/jdg.2021026
![]()
References:
| [1] |
S. Aghabozorgi, A. S. Shirkhorshidi and T. Y. Wah,
Time-series clustering–A decade review, Information Systems, 53 (2015), 16-38.
doi: 10.1016/j.is.2015.04.007. |
| [2] |
E. Alvarez, J. G. Brida and E. Limas,
Clustering of time series for the analysis of the COVID-19 pandemic evolution, Economics Bulletin, 41 (2021), 1082-1096.
|
| [3] |
K. Asahi, E. A. Undurraga, R. Valdés and R. Wagner, The effect of {COVID-19} on the economy: Evidence from an early adopter of localized lockdowns, medRxiv, (2020).
doi: 10.1101/2020.09.21.20198887. |
| [4] |
A. Ashofteh and J. M. Bravo,
A study on the quality of novel coronavirus (COVID-19) official datasets, Statistical J. IAOS, 36 (2020), 291-301.
doi: 10.3233/SJI-200674. |
| [5] |
V. D. Blondel, J.-L. Guillaume, R. Lambiotte and E. Lefebvre, Fast unfolding of communities in large networks, J. Statistical Mechanics: Theory and Experiment, (2008).
doi: 10.1088/1742-5468/2008/10/P10008. |
| [6] |
T. Caliński and J. A. Harabasz,
A dendrite method for cluster analysis, Comm. Statist., 3 (1974), 1-27.
doi: 10.1080/03610927408827101. |
| [7] |
V. Chandu, Identification of spatial variations in COVID-19 epidemiological data using k-means clustering algorithm: A global perspective, medRxiv, 2020.
doi: 10.1101/2020.06.03.20121194. |
| [8] |
G. Ciminelli and S. Garcia-Mandicó, Mitigation policies and emergency care management in Europe's ground zero for COVID-19, SSRN, (2020).
doi: 10.2139/ssrn.3604688. |
| [9] |
C. Costa-Santos, A. Luísa Neves, R. Correia, P. Santos and M. Monteiro-Soares, et al., COVID-19 surveillance - A descriptive study on data quality issues, medRxiv, (2020).
doi: 10.1101/2020.11.03.20225565. |
| [10] |
K. Degeling, N. N. Baxter, J. Emery, M. A. Jenkins and F. Franchini,
An inverse stage-shift model to estimate the excess mortality and health economic impact of delayed access to cancer services due to the COVID-19 pandemic, Asia-Pacific J. Clinical Oncology, 17 (2021), 359-367.
doi: 10.1111/ajco.13505. |
| [11] |
R. O. Duda and P. E. Hart, D. G. Stork, Pattern Classification and Scene Analysis, Wiley New York, 1973. |
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A. Fahim, Finding the number of clusters in data and better initial centers for k-means algorithm, Internat. J. Intelligent Systems & Applications, 12 (2020). |
| [13] |
G. Gan, C. Ma and J. Wu, Data Clustering. Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability, 20, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA; American Statistical Association, Alexandria, VA, 2007.
doi: 10.1137/1.9780898718348. |
| [14] |
A. Gandjour, How much reserve capacity is justifiable for hospital pandemic preparedness? A cost-effectiveness analysis for COVID-19 in Germany, medRxiv, (2020).
doi: 10.1101/2020.07.27.20162743. |
| [15] |
A. Z. Górski, S. Drożdż and J. Kwapień, Minimal spanning tree graphs and power like scaling in FOREX networks, preprint, arXiv: 0809.0437. |
| [16] |
M. O. Jackson, Social and Economic Networks, Princeton University Press, Princeton, NJ, 2010.
|
| [17] |
J. B. Kruskal Jr.,
On the shortest spanning subtree of a graph and the traveling salesman problem, Proc. Amer. Math. Soc., 7 (1956), 48-50.
doi: 10.1090/S0002-9939-1956-0078686-7. |
| [18] |
J. Kwapień, S. Gworek, S. Drożdż and A. Górski, Analysis of a network structure of the foreign currency exchange market, J. Econ. Interact. Coord., 4 (2009).
doi: 10.1007/s11403-009-0047-9. |
| [19] |
E. Limas,
An application of minimal spanning trees and hierarchical trees to the study of Latin American exchange rates, J. Dyn. Games, 6 (2019), 131-148.
doi: 10.3934/jdg.2019010. |
| [20] |
A. C. Mahasinghe, K. K. W. H. Erandi and S. S. N. Perera, An optimal lockdown relaxation strategy for minimizing the economic effects of COVID-19 outbreak, International J. Mathematics and Mathematical Sciences, 2021 (2021). |
| [21] |
R. N. Mantegna,
Hierarchical structure in financial markets, Eur. Phys. J. B - Condensed Matter and Complex Systems, 11 (1999), 193-197.
doi: 10.1007/s100510050929. |
| [22] |
R. N. Mantegna and H. Stanley, An Introduction to Econophysics. Correlations and Complexity in Finance, Cambridge University Press, Cambridge, 2007.
|
| [23] |
S. Milan and E. Treré, The rise of the data poor: The COVID-19 pandemic seen from the margins, Social Media + Society, 6 (2020).
doi: 10.1177/2056305120948233. |
| [24] |
F. Milani,
COVID-19 outbreak, social response, and early economic effects: A global VAR analysis of cross-country interdependencies, J. Population Economics, 34 (2021), 223-252.
doi: 10.1007/s00148-020-00792-4. |
| [25] |
B. W. Mol and J. Karnon, Strict lockdown versus flexible social distance strategy for COVID-19 disease: A cost-effectiveness analysis, medRxiv, (2020).
doi: 10.1101/2020.09.14.20194605. |
| [26] |
R. C. Prim,
Shortest connection networks and some generalizations, Bell System Tech. J., 36 (1957), 1389-1401.
doi: 10.1002/j.1538-7305.1957.tb01515.x. |
| [27] |
M. Rešovský, D. Horváth, V. Gazda and M. Siničáková, Minimum spanning tree application in the currency market, Biatec, 21 (2013), 21-23. Available from: https://www.nbs.sk/img/Documents/PUBLIK_NBS_FSR/Biatec/Rok2013/07-2013/05_biatec13-7_resovsky_EN.pdf. |
| [28] |
M. Roser, H. Ritchie, E. Ortiz-Ospina and J. Hasell, Coronavirus pandemic (COVID-19), Our World in Data, (2020). |
| [29] |
F. Santiago, C. De Fuentes, J. A. Peerally and J. Larsen,
Investing in innovative and productive capabilities for resilient economies in a post-COVID-19 world, Internat. J. Technological Learning, Innovation and Development, 12 (2020), 153-167.
doi: 10.1504/IJTLID.2020.110623. |
| [30] |
P. Schellekens and D. M. Sourrouille, COVID-19 mortality in rich and poor countries: A tale of two pandemics?, World Bank Policy Research Working Paper, No. 9260, (2020). |
| [31] |
D. Sherpa, Estimating impact of austerity policies in COVID-19 fatality rates: Examining the dynamics of economic policy and Case Fatality Rates (CFR) of COVID-19 in OECD countries, medRxiv, (2020).
doi: 10.2139/ssrn.3581274. |
| [32] |
J. A. Tenreiro Machado and A. M. Lopes,
Rare and extreme events: The case of COVID-19 pandemic, Nonlinear Dynamics, 100 (2020), 2953-2972.
doi: 10.1007/s11071-020-05680-w. |
| [33] |
G.-J. Wang, C. Xie, Y.-J. Chen and S. Chen,
Statistical properties of the foreign exchange network at different time scales: Evidence from detrended cross-correlation coefficient and minimum spanning tree, Entropy, 15 (2013), 1643-1662.
doi: 10.3390/e15051643. |
| [34] |
V. Zarikas, S. G. Poulopoulos, Z. Gareiou and E. Zervas, Clustering analysis of countries using the COVID-19 cases dataset, Data in Brief, 31 (2020).
doi: 10.1016/j.dib.2020.105787. |
show all references
References:
| [1] |
S. Aghabozorgi, A. S. Shirkhorshidi and T. Y. Wah,
Time-series clustering–A decade review, Information Systems, 53 (2015), 16-38.
doi: 10.1016/j.is.2015.04.007. |
| [2] |
E. Alvarez, J. G. Brida and E. Limas,
Clustering of time series for the analysis of the COVID-19 pandemic evolution, Economics Bulletin, 41 (2021), 1082-1096.
|
| [3] |
K. Asahi, E. A. Undurraga, R. Valdés and R. Wagner, The effect of {COVID-19} on the economy: Evidence from an early adopter of localized lockdowns, medRxiv, (2020).
doi: 10.1101/2020.09.21.20198887. |
| [4] |
A. Ashofteh and J. M. Bravo,
A study on the quality of novel coronavirus (COVID-19) official datasets, Statistical J. IAOS, 36 (2020), 291-301.
doi: 10.3233/SJI-200674. |
| [5] |
V. D. Blondel, J.-L. Guillaume, R. Lambiotte and E. Lefebvre, Fast unfolding of communities in large networks, J. Statistical Mechanics: Theory and Experiment, (2008).
doi: 10.1088/1742-5468/2008/10/P10008. |
| [6] |
T. Caliński and J. A. Harabasz,
A dendrite method for cluster analysis, Comm. Statist., 3 (1974), 1-27.
doi: 10.1080/03610927408827101. |
| [7] |
V. Chandu, Identification of spatial variations in COVID-19 epidemiological data using k-means clustering algorithm: A global perspective, medRxiv, 2020.
doi: 10.1101/2020.06.03.20121194. |
| [8] |
G. Ciminelli and S. Garcia-Mandicó, Mitigation policies and emergency care management in Europe's ground zero for COVID-19, SSRN, (2020).
doi: 10.2139/ssrn.3604688. |
| [9] |
C. Costa-Santos, A. Luísa Neves, R. Correia, P. Santos and M. Monteiro-Soares, et al., COVID-19 surveillance - A descriptive study on data quality issues, medRxiv, (2020).
doi: 10.1101/2020.11.03.20225565. |
| [10] |
K. Degeling, N. N. Baxter, J. Emery, M. A. Jenkins and F. Franchini,
An inverse stage-shift model to estimate the excess mortality and health economic impact of delayed access to cancer services due to the COVID-19 pandemic, Asia-Pacific J. Clinical Oncology, 17 (2021), 359-367.
doi: 10.1111/ajco.13505. |
| [11] |
R. O. Duda and P. E. Hart, D. G. Stork, Pattern Classification and Scene Analysis, Wiley New York, 1973. |
| [12] |
A. Fahim, Finding the number of clusters in data and better initial centers for k-means algorithm, Internat. J. Intelligent Systems & Applications, 12 (2020). |
| [13] |
G. Gan, C. Ma and J. Wu, Data Clustering. Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability, 20, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA; American Statistical Association, Alexandria, VA, 2007.
doi: 10.1137/1.9780898718348. |
| [14] |
A. Gandjour, How much reserve capacity is justifiable for hospital pandemic preparedness? A cost-effectiveness analysis for COVID-19 in Germany, medRxiv, (2020).
doi: 10.1101/2020.07.27.20162743. |
| [15] |
A. Z. Górski, S. Drożdż and J. Kwapień, Minimal spanning tree graphs and power like scaling in FOREX networks, preprint, arXiv: 0809.0437. |
| [16] |
M. O. Jackson, Social and Economic Networks, Princeton University Press, Princeton, NJ, 2010.
|
| [17] |
J. B. Kruskal Jr.,
On the shortest spanning subtree of a graph and the traveling salesman problem, Proc. Amer. Math. Soc., 7 (1956), 48-50.
doi: 10.1090/S0002-9939-1956-0078686-7. |
| [18] |
J. Kwapień, S. Gworek, S. Drożdż and A. Górski, Analysis of a network structure of the foreign currency exchange market, J. Econ. Interact. Coord., 4 (2009).
doi: 10.1007/s11403-009-0047-9. |
| [19] |
E. Limas,
An application of minimal spanning trees and hierarchical trees to the study of Latin American exchange rates, J. Dyn. Games, 6 (2019), 131-148.
doi: 10.3934/jdg.2019010. |
| [20] |
A. C. Mahasinghe, K. K. W. H. Erandi and S. S. N. Perera, An optimal lockdown relaxation strategy for minimizing the economic effects of COVID-19 outbreak, International J. Mathematics and Mathematical Sciences, 2021 (2021). |
| [21] |
R. N. Mantegna,
Hierarchical structure in financial markets, Eur. Phys. J. B - Condensed Matter and Complex Systems, 11 (1999), 193-197.
doi: 10.1007/s100510050929. |
| [22] |
R. N. Mantegna and H. Stanley, An Introduction to Econophysics. Correlations and Complexity in Finance, Cambridge University Press, Cambridge, 2007.
|
| [23] |
S. Milan and E. Treré, The rise of the data poor: The COVID-19 pandemic seen from the margins, Social Media + Society, 6 (2020).
doi: 10.1177/2056305120948233. |
| [24] |
F. Milani,
COVID-19 outbreak, social response, and early economic effects: A global VAR analysis of cross-country interdependencies, J. Population Economics, 34 (2021), 223-252.
doi: 10.1007/s00148-020-00792-4. |
| [25] |
B. W. Mol and J. Karnon, Strict lockdown versus flexible social distance strategy for COVID-19 disease: A cost-effectiveness analysis, medRxiv, (2020).
doi: 10.1101/2020.09.14.20194605. |
| [26] |
R. C. Prim,
Shortest connection networks and some generalizations, Bell System Tech. J., 36 (1957), 1389-1401.
doi: 10.1002/j.1538-7305.1957.tb01515.x. |
| [27] |
M. Rešovský, D. Horváth, V. Gazda and M. Siničáková, Minimum spanning tree application in the currency market, Biatec, 21 (2013), 21-23. Available from: https://www.nbs.sk/img/Documents/PUBLIK_NBS_FSR/Biatec/Rok2013/07-2013/05_biatec13-7_resovsky_EN.pdf. |
| [28] |
M. Roser, H. Ritchie, E. Ortiz-Ospina and J. Hasell, Coronavirus pandemic (COVID-19), Our World in Data, (2020). |
| [29] |
F. Santiago, C. De Fuentes, J. A. Peerally and J. Larsen,
Investing in innovative and productive capabilities for resilient economies in a post-COVID-19 world, Internat. J. Technological Learning, Innovation and Development, 12 (2020), 153-167.
doi: 10.1504/IJTLID.2020.110623. |
| [30] |
P. Schellekens and D. M. Sourrouille, COVID-19 mortality in rich and poor countries: A tale of two pandemics?, World Bank Policy Research Working Paper, No. 9260, (2020). |
| [31] |
D. Sherpa, Estimating impact of austerity policies in COVID-19 fatality rates: Examining the dynamics of economic policy and Case Fatality Rates (CFR) of COVID-19 in OECD countries, medRxiv, (2020).
doi: 10.2139/ssrn.3581274. |
| [32] |
J. A. Tenreiro Machado and A. M. Lopes,
Rare and extreme events: The case of COVID-19 pandemic, Nonlinear Dynamics, 100 (2020), 2953-2972.
doi: 10.1007/s11071-020-05680-w. |
| [33] |
G.-J. Wang, C. Xie, Y.-J. Chen and S. Chen,
Statistical properties of the foreign exchange network at different time scales: Evidence from detrended cross-correlation coefficient and minimum spanning tree, Entropy, 15 (2013), 1643-1662.
doi: 10.3390/e15051643. |
| [34] |
V. Zarikas, S. G. Poulopoulos, Z. Gareiou and E. Zervas, Clustering analysis of countries using the COVID-19 cases dataset, Data in Brief, 31 (2020).
doi: 10.1016/j.dib.2020.105787. |
Figure 2.
Number of identified groups and MST average path in each time window. Source: own construction based on OWID data
Figure 3.
Number of times two countries where in the same group per time window. Source: own construction based on OWID data
Figure 4.
Intensity of similarity in the disease evolution network. Source: own construction based on OWID data. Appendix B presents countries corresponding to each number
Figure 5.
Rolling 7-day average of daily new confirmed COVID-19 cases per million people by country and community. Source: own construction based on OWID data. Black central lines correspond to group median; high and low black lines represent the upper and lower quartiles respectively
Figure 6.
Communities map. Source: own construction based on OWID data
Table 1.
Communities' demographic and socioeconomic data. Source: own construction based on OWID data. Note: Difference between sum of countries frequency and total countries per group at each variable are due to lack of data. *Measure in constant 2011 international dollars, most recent year available
| C 1 | C 2 | C 3 | ||
| Average population density | 326 | 182 | 96 | |
| Number of countries per | 1 | 14 | 15 | 1 |
| quartile of population | 2 | 10 | 16 | 5 |
| density | 3 | 13 | 16 | 2 |
| 4 | 19 | 11 | 1 | |
| Median age | 33 | 35 | 29 | |
| Number of countries per | 1 | 16 | 11 | 3 |
| quartile of median age | 2 | 13 | 14 | 3 |
| 3 | 10 | 19 | 1 | |
| 4 | 14 | 15 | 1 | |
| Average GDP per capita* | 20545 | 26327 | 11196 | |
| Number of countries per | 1 | 16 | 9 | 4 |
| quartile of GDP per capita | 2 | 15 | 13 | 2 |
| 3 | 12 | 16 | 3 | |
| 4 | 11 | 19 | 0 | |
| Average life expectancy | 75 | 76 | 72 | |
| Number of countries per | 1 | 14 | 12 | 5 |
| quartile of life expectancy | 2 | 15 | 15 | 1 |
| 3 | 14 | 14 | 3 | |
| 4 | 13 | 18 | 0 | |
| Av. Human Development Index | 32.7 | 34.6 | 29.5 | |
| Number of countries per | 1 | 17 | 11 | 3 |
| quartile of Human | 2 | 16 | 12 | 2 |
| Development Index | 3 | 9 | 18 | 3 |
| 4 | 14 | 17 | 0 |
| C 1 | C 2 | C 3 | ||
| Average population density | 326 | 182 | 96 | |
| Number of countries per | 1 | 14 | 15 | 1 |
| quartile of population | 2 | 10 | 16 | 5 |
| density | 3 | 13 | 16 | 2 |
| 4 | 19 | 11 | 1 | |
| Median age | 33 | 35 | 29 | |
| Number of countries per | 1 | 16 | 11 | 3 |
| quartile of median age | 2 | 13 | 14 | 3 |
| 3 | 10 | 19 | 1 | |
| 4 | 14 | 15 | 1 | |
| Average GDP per capita* | 20545 | 26327 | 11196 | |
| Number of countries per | 1 | 16 | 9 | 4 |
| quartile of GDP per capita | 2 | 15 | 13 | 2 |
| 3 | 12 | 16 | 3 | |
| 4 | 11 | 19 | 0 | |
| Average life expectancy | 75 | 76 | 72 | |
| Number of countries per | 1 | 14 | 12 | 5 |
| quartile of life expectancy | 2 | 15 | 15 | 1 |
| 3 | 14 | 14 | 3 | |
| 4 | 13 | 18 | 0 | |
| Av. Human Development Index | 32.7 | 34.6 | 29.5 | |
| Number of countries per | 1 | 17 | 11 | 3 |
| quartile of Human | 2 | 16 | 12 | 2 |
| Development Index | 3 | 9 | 18 | 3 |
| 4 | 14 | 17 | 0 |
Table 2.
Communities geographic data. Source: own construction based on OWID data
| Communities | No. of countries by continent | |||||||
| Countries | Africa | Asia | Europe | North America | Oceania | South America | Islands | |
| 1 | 56 | 11 | 17 | 14 | 8 | 1 | 5 | 9 |
| 2 | 59 | 7 | 18 | 24 | 5 | 1 | 4 | 10 |
| 3 | 9 | 3 | 2 | 3 | 1 | 0 | 0 | 0 |
| Total | 124 | 21 | 37 | 41 | 14 | 2 | 9 | 19 |
| Communities | No. of countries by continent | |||||||
| Countries | Africa | Asia | Europe | North America | Oceania | South America | Islands | |
| 1 | 56 | 11 | 17 | 14 | 8 | 1 | 5 | 9 |
| 2 | 59 | 7 | 18 | 24 | 5 | 1 | 4 | 10 |
| 3 | 9 | 3 | 2 | 3 | 1 | 0 | 0 | 0 |
| Total | 124 | 21 | 37 | 41 | 14 | 2 | 9 | 19 |
Table 3.
Countries actions in relation to COVID – 19. Source: own construction based on OWID data. Note: Difference between sum of countries frequency and total countries per group at each variable are due to lack of data.
Testing policy codebook: 0 – No testing policy, 1-Only those who both (a) have symptoms and also (b) meet specific criteria (e.g. key workers, admitted to hospital, came into contact with a known case, returned from overseas), 2-Testing of anyone showing COVID-19 symptoms, 3-Open public testing (e.g. "drive through" testing available to asymptomatic people). *Any country of the sample had category 0 as predominant testing policy. Tracing policy codebook: 0 – No tracing, 1 -Some, but not all, cases are traced, 2 – All cases are traced
Testing policy codebook: 0 – No testing policy, 1-Only those who both (a) have symptoms and also (b) meet specific criteria (e.g. key workers, admitted to hospital, came into contact with a known case, returned from overseas), 2-Testing of anyone showing COVID-19 symptoms, 3-Open public testing (e.g. "drive through" testing available to asymptomatic people). *Any country of the sample had category 0 as predominant testing policy. Tracing policy codebook: 0 – No tracing, 1 -Some, but not all, cases are traced, 2 – All cases are traced
| C 1 | C 2 | C 3 | ||
| Average Max. Government Response Index | 77 | 76 | 75 | |
| Number of countries per quartile | 1 | 10 | 16 | 3 |
| of Max. Government Response Index | 2 | 18 | 8 | 2 |
| 3 | 15 | 13 | 1 | |
| 4 | 10 | 17 | 2 | |
| Average Max. Containment Health Index | 80 | 79 | 77 | |
| Number of countries per quartile | 1 | 10 | 16 | 3 |
| of Max. Containment Health Index | 2 | 14 | 8 | 2 |
| 3 | 18 | 14 | 1 | |
| 4 | 11 | 16 | 2 | |
| Average Max. Economic Support Index | 65 | 68 | 70 | |
| Number of countries per quartile | 1 | 7 | 8 | 1 |
| of Max. Economic Support Index | 2 | 20 | 16 | 2 |
| 3 | 15 | 14 | 3 | |
| 4 | 11 | 16 | 2 | |
| Predominant testing policy* | 2 | 2 | 1 | |
| Number of countries per | 1 | 14 | 14 | 5 |
| predominant testing policy | 2 | 25 | 23 | 3 |
| 3 | 15 | 18 | 0 | |
| Predominant contact tracing policy | 2 | 2 | 1 | |
| Number of countries per predominant | 0 | 4 | 3 | 0 |
| contact tracing policy | 1 | 19 | 11 | 6 |
| 2 | 31 | 41 | 2 |
| C 1 | C 2 | C 3 | ||
| Average Max. Government Response Index | 77 | 76 | 75 | |
| Number of countries per quartile | 1 | 10 | 16 | 3 |
| of Max. Government Response Index | 2 | 18 | 8 | 2 |
| 3 | 15 | 13 | 1 | |
| 4 | 10 | 17 | 2 | |
| Average Max. Containment Health Index | 80 | 79 | 77 | |
| Number of countries per quartile | 1 | 10 | 16 | 3 |
| of Max. Containment Health Index | 2 | 14 | 8 | 2 |
| 3 | 18 | 14 | 1 | |
| 4 | 11 | 16 | 2 | |
| Average Max. Economic Support Index | 65 | 68 | 70 | |
| Number of countries per quartile | 1 | 7 | 8 | 1 |
| of Max. Economic Support Index | 2 | 20 | 16 | 2 |
| 3 | 15 | 14 | 3 | |
| 4 | 11 | 16 | 2 | |
| Predominant testing policy* | 2 | 2 | 1 | |
| Number of countries per | 1 | 14 | 14 | 5 |
| predominant testing policy | 2 | 25 | 23 | 3 |
| 3 | 15 | 18 | 0 | |
| Predominant contact tracing policy | 2 | 2 | 1 | |
| Number of countries per predominant | 0 | 4 | 3 | 0 |
| contact tracing policy | 1 | 19 | 11 | 6 |
| 2 | 31 | 41 | 2 |
Table 4.
Healthcare system and deaths per million. Source: own construction based on OWID data. Note: The difference between the sum of the countries frequency and total countries per group at each variable are due to lack of data
| C 1 | C 2 | C 3 | ||
| Average Healthcare access | 68 | 72 | 61 | |
| and quality index | ||||
| Number of countries per | 1 | 15 | 12 | 3 |
| quartile of Healthcare access | 2 | 18 | 10 | 3 |
| and quality index | 3 | 9 | 19 | 2 |
| 4 | 13 | 18 | 0 | |
| Average deaths per million | 289 | 285 | 281 | |
| at December 1st | ||||
| Number of countries per | 1 | 16 | 9 | 3 |
| quartile of deaths per million | 2 | 12 | 17 | 2 |
| by December 1st | 3 | 10 | 20 | 1 |
| 4 | 16 | 12 | 3 |
| C 1 | C 2 | C 3 | ||
| Average Healthcare access | 68 | 72 | 61 | |
| and quality index | ||||
| Number of countries per | 1 | 15 | 12 | 3 |
| quartile of Healthcare access | 2 | 18 | 10 | 3 |
| and quality index | 3 | 9 | 19 | 2 |
| 4 | 13 | 18 | 0 | |
| Average deaths per million | 289 | 285 | 281 | |
| at December 1st | ||||
| Number of countries per | 1 | 16 | 9 | 3 |
| quartile of deaths per million | 2 | 12 | 17 | 2 |
| by December 1st | 3 | 10 | 20 | 1 |
| 4 | 16 | 12 | 3 |
Table 5.
Country community changes by allowed group maximum in each time window. Country community changes by allowed group maximum in each time window
| Max. No. of groups per time window | |||
| 30 | 20 | 10 | |
| Belgium | 1 | 1 | 2 |
| Bolivia | 1 | 3 | 1 |
| Bangladesh | 1 | 2 | 2 |
| Andorra | 1 | 3 | 1 |
| Chile | 1 | 1 | 2 |
| Morocco | 1 | 1 | 2 |
| Togo | 1 | 3 | 3 |
| Australia | 2 | 3 | 3 |
| South Africa | 2 | 3 | 3 |
| Montenegro | 3 | 2 | 3 |
| Max. No. of groups per time window | |||
| 30 | 20 | 10 | |
| Belgium | 1 | 1 | 2 |
| Bolivia | 1 | 3 | 1 |
| Bangladesh | 1 | 2 | 2 |
| Andorra | 1 | 3 | 1 |
| Chile | 1 | 1 | 2 |
| Morocco | 1 | 1 | 2 |
| Togo | 1 | 3 | 3 |
| Australia | 2 | 3 | 3 |
| South Africa | 2 | 3 | 3 |
| Montenegro | 3 | 2 | 3 |
Table 6.
Countries' data. Source: own construction based on OWID data. Reference: (1) Population density; (2): Median age; (3): GDP per capita; (4): Life expectancy; (5): Human Development Index; (6): Max Government response index
| Country | Cluster | Continent | (1) | (2) | (3) | (4) | (5) | (6) |
| Uruguay | 1 | South America | 19.75 | 35.60 | 20551 | 77.91 | 0.80 | 61.31 |
| Azerbaijan | 1 | Asia | 119.31 | 32.40 | 15847 | 73.00 | 0.76 | 86.31 |
| Burkina Faso | 1 | Africa | 70.15 | 17.60 | 1703 | 61.58 | 0.42 | 72.62 |
| Belgium | 1 | Europe | 375.56 | 41.80 | 42659 | 81.63 | 0.92 | 76.79 |
| Bosnia&Herzegovina | 1 | Europe | 68.50 | 42.50 | 11714 | 77.40 | 0.77 | 75.00 |
| Bolivia | 1 | South America | 10.20 | 25.40 | 6886 | 71.51 | 0.69 | 76.79 |
| Bangladesh | 1 | Asia | 1265.04 | 27.50 | 3524 | 72.59 | 0.61 | 82.74 |
| China | 1 | Asia | 147.67 | 38.70 | 15309 | 76.91 | 0.75 | 82.14 |
| Switzerland | 1 | Europe | 214.24 | 43.10 | 57410 | 83.78 | 0.94 | 61.90 |
| Germany | 1 | Europe | 237.02 | 46.60 | 45229 | 81.33 | 0.94 | 72.62 |
| Dominica | 1 | North America | 98.57 | - | 9673 | 75.00 | 0.72 | 76.79 |
| Dominican Republic | 1 | North America | 222.87 | 27.60 | 14601 | 74.08 | 0.74 | 81.55 |
| Andorra | 1 | Europe | 163.76 | - | - | 83.73 | 0.86 | 72.02 |
| Algeria | 1 | Africa | 17.35 | 29.10 | 13914 | 76.88 | 0.75 | 78.57 |
| Ghana | 1 | Africa | 126.72 | 21.10 | 4228 | 64.07 | 0.59 | 76.79 |
| Spain | 1 | Europe | 93.11 | 45.50 | 34272 | 83.56 | 0.89 | 77.98 |
| Greece | 1 | Europe | 83.48 | 45.30 | 24574 | 82.24 | 0.87 | 84.52 |
| Guatemala | 1 | North America | 157.83 | 22.90 | 7424 | 74.30 | 0.65 | 82.74 |
| India | 1 | Asia | 450.42 | 28.20 | 6427 | 69.66 | 0.64 | 95.54 |
| Japan | 1 | Asia | 347.78 | 48.20 | 39002 | 84.63 | 0.91 | 51.79 |
| Kyrgyzstan | 1 | Asia | 32.33 | 26.30 | 3393 | 71.45 | 0.67 | - |
| Canada | 1 | North America | 4.04 | 41.40 | 44018 | 82.43 | 0.93 | 72.62 |
| Kenya | 1 | Africa | 87.32 | 20.00 | 2993 | 66.70 | 0.59 | 82.14 |
| Armenia | 1 | Asia | 102.93 | 35.70 | 8788 | 75.09 | 0.76 | - |
| Barbados | 1 | North America | 664.46 | 39.80 | 16978 | 79.19 | 0.80 | 77.98 |
| Kazakhstan | 1 | Asia | 6.68 | 30.60 | 24056 | 73.60 | 0.80 | 78.57 |
| Slovenia | 1 | Europe | 102.62 | 44.50 | 31401 | 81.32 | 0.90 | 83.93 |
| United States | 1 | North America | 35.61 | 38.30 | 54225 | 78.86 | 0.92 | 74.40 |
| Liechtenstein | 1 | Europe | 237.01 | - | - | 82.49 | 0.92 | - |
| Madagascar | 1 | Africa | 43.95 | 19.60 | 1416 | 67.04 | 0.52 | 67.26 |
| Bulgaria | 1 | Europe | 65.18 | 44.70 | 18563 | 75.05 | 0.81 | 74.40 |
| Jamaica | 1 | North America | 266.88 | 31.40 | 8194 | 74.47 | 0.73 | 74.40 |
| Lebanon | 1 | Asia | 594.56 | 31.10 | 13368 | 78.93 | 0.76 | 73.21 |
| Chile | 1 | South America | 24.28 | 35.40 | 22767 | 80.18 | 0.84 | 84.82 |
| Mexico | 1 | North America | 66.44 | 29.30 | 17336 | 75.05 | 0.77 | 69.05 |
| Morocco | 1 | Africa | 80.08 | 29.60 | 7485 | 76.68 | 0.67 | 86.31 |
| Sri Lanka | 1 | Asia | 341.96 | 34.10 | 11669 | 76.98 | 0.77 | 79.17 |
| Mongolia | 1 | Asia | 1.98 | 28.60 | 11841 | 69.87 | 0.74 | 82.74 |
| Nigeria | 1 | Africa | 209.59 | 18.10 | 5338 | 54.69 | 0.53 | 70.83 |
| Colombia | 1 | South America | 44.22 | 32.20 | 13255 | 77.29 | 0.75 | 88.10 |
| Netherlands | 1 | Europe | 508.54 | 43.20 | 48473 | 82.28 | 0.93 | 66.67 |
| New Zealand | 1 | Oceania | 18.21 | 37.90 | 36086 | 82.29 | 0.92 | 82.74 |
| Afghanistan | 1 | Asia | 54.42 | 18.60 | 1804 | 64.83 | 0.50 | 65.48 |
| Poland | 1 | Europe | 124.03 | 41.80 | 27216 | 78.73 | 0.87 | 73.81 |
| Paraguay | 1 | South America | 17.14 | 26.50 | 8827 | 74.25 | 0.70 | 79.76 |
| Palestine | 1 | Asia | 778.20 | 20.40 | 4450 | 74.05 | 0.69 | 75.00 |
| Qatar | 1 | Asia | 227.32 | 31.90 | 116936 | 80.23 | 0.86 | 82.14 |
| Romania | 1 | Europe | 85.13 | 43.00 | 23313 | 76.05 | 0.81 | 80.95 |
| Rwanda | 1 | Africa | 494.87 | 20.30 | 1854 | 69.02 | 0.52 | 85.12 |
| Egypt | 1 | Africa | 98.00 | 25.30 | 10550 | 71.99 | 0.70 | 80.36 |
| Russia | 1 | Europe | 8.82 | 39.60 | 24766 | 72.58 | 0.82 | 77.98 |
| Thailand | 1 | Asia | 135.13 | 40.10 | 16278 | 77.15 | 0.76 | 80.06 |
| Togo | 1 | Africa | 143.37 | 19.40 | 1430 | 61.04 | 0.50 | 69.05 |
| Vietnam | 1 | Asia | 308.13 | 32.60 | 6172 | 75.40 | 0.69 | 83.33 |
| Singapore | 1 | Asia | 7915.73 | 42.40 | 85535 | 83.62 | 0.93 | 85.71 |
| Zambia | 1 | Africa | 23.00 | 17.70 | 3689 | 63.89 | 0.59 | 63.99 |
| Albania | 2 | Europe | 104.87 | 38.00 | 11803 | 78.57 | 0.79 | 75.60 |
| Australia | 2 | Oceania | 3.20 | 37.90 | 44649 | 83.44 | 0.94 | 82.14 |
| Austria | 2 | Europe | 106.75 | 44.40 | 45437 | 81.54 | 0.91 | 86.90 |
| Brunei | 2 | Asia | 81.35 | 32.40 | 71809 | 75.86 | 0.85 | 49.40 |
| Brazil | 2 | South America | 25.04 | 33.50 | 14103 | 75.88 | 0.76 | 77.38 |
| Cote d'Ivoire | 2 | Africa | 76.40 | 18.70 | 3601 | 57.78 | 0.49 | 68.15 |
| Argentina | 2 | South America | 16.18 | 31.90 | 18934 | 76.67 | 0.83 | 89.88 |
| Denmark | 2 | Europe | 136.52 | 42.30 | 46683 | 80.90 | 0.93 | 69.05 |
| Belarus | 2 | Europe | 46.86 | 40.30 | 17168 | 74.79 | 0.81 | 30.36 |
| Estonia | 2 | Europe | 31.03 | 42.70 | 29481 | 78.74 | 0.87 | 65.48 |
| Costa Rica | 2 | North America | 96.08 | 33.60 | 15525 | 80.28 | 0.79 | 69.05 |
| Honduras | 2 | North America | 82.81 | 24.90 | 4542 | 75.27 | 0.62 | 88.10 |
| Serbia | 2 | Europe | 80.29 | 41.20 | 14049 | 76.00 | 0.79 | 79.17 |
| Hungary | 2 | Europe | 108.04 | 43.40 | 26778 | 76.88 | 0.84 | 74.11 |
| Ireland | 2 | Europe | 69.87 | 38.70 | 67335 | 82.30 | 0.94 | 82.14 |
| Croatia | 2 | Europe | 73.73 | 44.00 | 22670 | 78.49 | 0.83 | 86.31 |
| Iceland | 2 | Europe | 3.40 | 37.30 | 46483 | 82.99 | 0.94 | 64.29 |
| Italy | 2 | Europe | 205.86 | 47.90 | 35220 | 83.51 | 0.88 | 85.42 |
| El Salvador | 2 | North America | 307.81 | 27.60 | 7292 | 73.32 | 0.67 | 91.07 |
| Peru | 2 | South America | 25.13 | 29.10 | 12237 | 76.74 | 0.75 | 86.31 |
| Slovakia | 2 | Europe | 113.13 | 41.20 | 30155 | 77.54 | 0.86 | - |
| Cyprus | 2 | Europe | 127.66 | 37.30 | 32415 | 80.98 | 0.87 | 89.29 |
| Iran | 2 | Asia | 49.83 | 32.40 | 19083 | 76.68 | 0.80 | 63.99 |
| Cambodia | 2 | Asia | 90.67 | 25.60 | 3645 | 69.82 | 0.58 | 51.79 |
| Kuwait | 2 | Asia | 232.13 | 33.70 | 65531 | 75.49 | 0.80 | 86.90 |
| Portugal | 2 | Europe | 112.37 | 46.20 | 27937 | 82.05 | 0.85 | 81.55 |
| United Arab Emirates | 2 | Asia | 112.44 | 34.00 | 67293 | 77.97 | 0.86 | 86.31 |
| Djibouti | 2 | Africa | 41.29 | 25.40 | 2705 | 67.11 | 0.48 | 82.14 |
| Equatorial Guinea | 2 | Africa | 45.19 | 22.40 | 22605 | 58.74 | 0.59 | - |
| Israel | 2 | Asia | 402.61 | 30.60 | 33132 | 82.97 | 0.90 | 89.88 |
| South Korea | 2 | Asia | 527.97 | 43.40 | 35938 | 83.03 | 0.90 | 76.19 |
| Maldives | 2 | Asia | 1454.43 | 30.60 | 15184 | 78.92 | 0.72 | - |
| Bahrain | 2 | Asia | 1935.91 | 32.40 | 43291 | 77.29 | 0.85 | 80.36 |
| United Kingdom | 2 | Europe | 272.90 | 40.80 | 39753 | 81.32 | 0.92 | 74.70 |
| Philippines | 2 | Asia | 351.87 | 25.20 | 7599 | 71.23 | 0.70 | 85.12 |
| Moldova | 2 | Europe | 123.66 | 37.60 | 5190 | 71.90 | 0.70 | 75.00 |
| Macedonia | 2 | Europe | 82.60 | 39.10 | 13111 | 75.80 | 0.76 | - |
| Finland | 2 | Europe | 18.14 | 42.80 | 40586 | 81.91 | 0.92 | 56.55 |
| Malta | 2 | Europe | 1454.04 | 42.40 | 36513 | 82.53 | 0.88 | - |
| Malaysia | 2 | Asia | 96.25 | 29.90 | 26808 | 76.16 | 0.80 | 81.85 |
| Cuba | 2 | North America | 110.41 | 43.10 | 0 | 78.80 | 0.78 | 82.14 |
| Mali | 2 | Africa | 15.20 | 16.40 | 2014 | 59.31 | 0.43 | 66.67 |
| Oman | 2 | Asia | 14.98 | 30.70 | 37961 | 77.86 | 0.82 | 88.69 |
| Saudi Arabia | 2 | Asia | 15.32 | 31.90 | 49045 | 75.13 | 0.85 | 87.50 |
| France | 2 | Europe | 122.58 | 42.00 | 38606 | 82.66 | 0.90 | 79.17 |
| Iraq | 2 | Asia | 88.13 | 20.00 | 15664 | 70.60 | 0.69 | 82.14 |
| Norway | 2 | Europe | 14.46 | 39.70 | 64800 | 82.40 | 0.95 | 69.64 |
| Senegal | 2 | Africa | 82.33 | 18.70 | 2471 | 67.94 | 0.51 | 70.83 |
| Turkey | 2 | Asia | 104.91 | 31.60 | 25129 | 77.69 | 0.79 | 78.87 |
| Trinidad and Tobago | 2 | North America | 266.89 | 36.20 | 28763 | 73.51 | 0.78 | 82.74 |
| Georgia | 2 | Asia | 65.03 | 38.70 | 9745 | 73.77 | 0.78 | 85.12 |
| Taiwan | 2 | Asia | 0.00 | 42.20 | - | 80.46 | - | 44.64 |
| Ukraine | 2 | Europe | 77.39 | 41.40 | 7894 | 72.06 | 0.75 | 79.76 |
| Venezuela | 2 | South America | 36.25 | 29.00 | 16745 | 72.06 | 0.76 | 77.98 |
| D. R. of Congo | 2 | Africa | 35.88 | 17.00 | 808 | 60.68 | 0.46 | 71.13 |
| Czech Republic | 2 | Europe | 137.18 | 43.30 | 32606 | 79.38 | 0.89 | 84.52 |
| Indonesia | 2 | Asia | 145.73 | 29.30 | 11189 | 71.72 | 0.69 | 68.15 |
| Sweden | 2 | Europe | 24.72 | 41.00 | 46949 | 82.80 | 0.93 | 55.36 |
| South Africa | 2 | Africa | 46.75 | 27.30 | 12295 | 64.13 | 0.70 | 84.52 |
| Cameroon | 3 | Africa | 50.89 | 18.80 | 3365 | 59.29 | 0.56 | 59.52 |
| Ethiopia | 3 | Africa | 104.96 | 19.80 | 1730 | 66.60 | 0.46 | 70.24 |
| Latvia | 3 | Europe | 31.21 | 43.90 | 25064 | 75.29 | 0.85 | 64.88 |
| Montenegro | 3 | Europe | 46.28 | 39.10 | 16409 | 76.88 | 0.81 | - |
| Pakistan | 3 | Asia | 255.57 | 23.50 | 5035 | 67.27 | 0.56 | 77.38 |
| Panama | 3 | North America | 55.13 | 29.70 | 22267 | 78.51 | 0.79 | 85.71 |
| Tunisia | 3 | Africa | 74.23 | 32.70 | 10849 | 76.70 | 0.74 | 76.19 |
| Uzbekistan | 3 | Asia | 76.13 | 28.20 | 6253 | 71.72 | 0.71 | 83.04 |
| Kosovo | 3 | Europe | 168.16 | - | 9796 | 71.95 | - | 79.76 |
| Country | Cluster | Continent | (1) | (2) | (3) | (4) | (5) | (6) |
| Uruguay | 1 | South America | 19.75 | 35.60 | 20551 | 77.91 | 0.80 | 61.31 |
| Azerbaijan | 1 | Asia | 119.31 | 32.40 | 15847 | 73.00 | 0.76 | 86.31 |
| Burkina Faso | 1 | Africa | 70.15 | 17.60 | 1703 | 61.58 | 0.42 | 72.62 |
| Belgium | 1 | Europe | 375.56 | 41.80 | 42659 | 81.63 | 0.92 | 76.79 |
| Bosnia&Herzegovina | 1 | Europe | 68.50 | 42.50 | 11714 | 77.40 | 0.77 | 75.00 |
| Bolivia | 1 | South America | 10.20 | 25.40 | 6886 | 71.51 | 0.69 | 76.79 |
| Bangladesh | 1 | Asia | 1265.04 | 27.50 | 3524 | 72.59 | 0.61 | 82.74 |
| China | 1 | Asia | 147.67 | 38.70 | 15309 | 76.91 | 0.75 | 82.14 |
| Switzerland | 1 | Europe | 214.24 | 43.10 | 57410 | 83.78 | 0.94 | 61.90 |
| Germany | 1 | Europe | 237.02 | 46.60 | 45229 | 81.33 | 0.94 | 72.62 |
| Dominica | 1 | North America | 98.57 | - | 9673 | 75.00 | 0.72 | 76.79 |
| Dominican Republic | 1 | North America | 222.87 | 27.60 | 14601 | 74.08 | 0.74 | 81.55 |
| Andorra | 1 | Europe | 163.76 | - | - | 83.73 | 0.86 | 72.02 |
| Algeria | 1 | Africa | 17.35 | 29.10 | 13914 | 76.88 | 0.75 | 78.57 |
| Ghana | 1 | Africa | 126.72 | 21.10 | 4228 | 64.07 | 0.59 | 76.79 |
| Spain | 1 | Europe | 93.11 | 45.50 | 34272 | 83.56 | 0.89 | 77.98 |
| Greece | 1 | Europe | 83.48 | 45.30 | 24574 | 82.24 | 0.87 | 84.52 |
| Guatemala | 1 | North America | 157.83 | 22.90 | 7424 | 74.30 | 0.65 | 82.74 |
| India | 1 | Asia | 450.42 | 28.20 | 6427 | 69.66 | 0.64 | 95.54 |
| Japan | 1 | Asia | 347.78 | 48.20 | 39002 | 84.63 | 0.91 | 51.79 |
| Kyrgyzstan | 1 | Asia | 32.33 | 26.30 | 3393 | 71.45 | 0.67 | - |
| Canada | 1 | North America | 4.04 | 41.40 | 44018 | 82.43 | 0.93 | 72.62 |
| Kenya | 1 | Africa | 87.32 | 20.00 | 2993 | 66.70 | 0.59 | 82.14 |
| Armenia | 1 | Asia | 102.93 | 35.70 | 8788 | 75.09 | 0.76 | - |
| Barbados | 1 | North America | 664.46 | 39.80 | 16978 | 79.19 | 0.80 | 77.98 |
| Kazakhstan | 1 | Asia | 6.68 | 30.60 | 24056 | 73.60 | 0.80 | 78.57 |
| Slovenia | 1 | Europe | 102.62 | 44.50 | 31401 | 81.32 | 0.90 | 83.93 |
| United States | 1 | North America | 35.61 | 38.30 | 54225 | 78.86 | 0.92 | 74.40 |
| Liechtenstein | 1 | Europe | 237.01 | - | - | 82.49 | 0.92 | - |
| Madagascar | 1 | Africa | 43.95 | 19.60 | 1416 | 67.04 | 0.52 | 67.26 |
| Bulgaria | 1 | Europe | 65.18 | 44.70 | 18563 | 75.05 | 0.81 | 74.40 |
| Jamaica | 1 | North America | 266.88 | 31.40 | 8194 | 74.47 | 0.73 | 74.40 |
| Lebanon | 1 | Asia | 594.56 | 31.10 | 13368 | 78.93 | 0.76 | 73.21 |
| Chile | 1 | South America | 24.28 | 35.40 | 22767 | 80.18 | 0.84 | 84.82 |
| Mexico | 1 | North America | 66.44 | 29.30 | 17336 | 75.05 | 0.77 | 69.05 |
| Morocco | 1 | Africa | 80.08 | 29.60 | 7485 | 76.68 | 0.67 | 86.31 |
| Sri Lanka | 1 | Asia | 341.96 | 34.10 | 11669 | 76.98 | 0.77 | 79.17 |
| Mongolia | 1 | Asia | 1.98 | 28.60 | 11841 | 69.87 | 0.74 | 82.74 |
| Nigeria | 1 | Africa | 209.59 | 18.10 | 5338 | 54.69 | 0.53 | 70.83 |
| Colombia | 1 | South America | 44.22 | 32.20 | 13255 | 77.29 | 0.75 | 88.10 |
| Netherlands | 1 | Europe | 508.54 | 43.20 | 48473 | 82.28 | 0.93 | 66.67 |
| New Zealand | 1 | Oceania | 18.21 | 37.90 | 36086 | 82.29 | 0.92 | 82.74 |
| Afghanistan | 1 | Asia | 54.42 | 18.60 | 1804 | 64.83 | 0.50 | 65.48 |
| Poland | 1 | Europe | 124.03 | 41.80 | 27216 | 78.73 | 0.87 | 73.81 |
| Paraguay | 1 | South America | 17.14 | 26.50 | 8827 | 74.25 | 0.70 | 79.76 |
| Palestine | 1 | Asia | 778.20 | 20.40 | 4450 | 74.05 | 0.69 | 75.00 |
| Qatar | 1 | Asia | 227.32 | 31.90 | 116936 | 80.23 | 0.86 | 82.14 |
| Romania | 1 | Europe | 85.13 | 43.00 | 23313 | 76.05 | 0.81 | 80.95 |
| Rwanda | 1 | Africa | 494.87 | 20.30 | 1854 | 69.02 | 0.52 | 85.12 |
| Egypt | 1 | Africa | 98.00 | 25.30 | 10550 | 71.99 | 0.70 | 80.36 |
| Russia | 1 | Europe | 8.82 | 39.60 | 24766 | 72.58 | 0.82 | 77.98 |
| Thailand | 1 | Asia | 135.13 | 40.10 | 16278 | 77.15 | 0.76 | 80.06 |
| Togo | 1 | Africa | 143.37 | 19.40 | 1430 | 61.04 | 0.50 | 69.05 |
| Vietnam | 1 | Asia | 308.13 | 32.60 | 6172 | 75.40 | 0.69 | 83.33 |
| Singapore | 1 | Asia | 7915.73 | 42.40 | 85535 | 83.62 | 0.93 | 85.71 |
| Zambia | 1 | Africa | 23.00 | 17.70 | 3689 | 63.89 | 0.59 | 63.99 |
| Albania | 2 | Europe | 104.87 | 38.00 | 11803 | 78.57 | 0.79 | 75.60 |
| Australia | 2 | Oceania | 3.20 | 37.90 | 44649 | 83.44 | 0.94 | 82.14 |
| Austria | 2 | Europe | 106.75 | 44.40 | 45437 | 81.54 | 0.91 | 86.90 |
| Brunei | 2 | Asia | 81.35 | 32.40 | 71809 | 75.86 | 0.85 | 49.40 |
| Brazil | 2 | South America | 25.04 | 33.50 | 14103 | 75.88 | 0.76 | 77.38 |
| Cote d'Ivoire | 2 | Africa | 76.40 | 18.70 | 3601 | 57.78 | 0.49 | 68.15 |
| Argentina | 2 | South America | 16.18 | 31.90 | 18934 | 76.67 | 0.83 | 89.88 |
| Denmark | 2 | Europe | 136.52 | 42.30 | 46683 | 80.90 | 0.93 | 69.05 |
| Belarus | 2 | Europe | 46.86 | 40.30 | 17168 | 74.79 | 0.81 | 30.36 |
| Estonia | 2 | Europe | 31.03 | 42.70 | 29481 | 78.74 | 0.87 | 65.48 |
| Costa Rica | 2 | North America | 96.08 | 33.60 | 15525 | 80.28 | 0.79 | 69.05 |
| Honduras | 2 | North America | 82.81 | 24.90 | 4542 | 75.27 | 0.62 | 88.10 |
| Serbia | 2 | Europe | 80.29 | 41.20 | 14049 | 76.00 | 0.79 | 79.17 |
| Hungary | 2 | Europe | 108.04 | 43.40 | 26778 | 76.88 | 0.84 | 74.11 |
| Ireland | 2 | Europe | 69.87 | 38.70 | 67335 | 82.30 | 0.94 | 82.14 |
| Croatia | 2 | Europe | 73.73 | 44.00 | 22670 | 78.49 | 0.83 | 86.31 |
| Iceland | 2 | Europe | 3.40 | 37.30 | 46483 | 82.99 | 0.94 | 64.29 |
| Italy | 2 | Europe | 205.86 | 47.90 | 35220 | 83.51 | 0.88 | 85.42 |
| El Salvador | 2 | North America | 307.81 | 27.60 | 7292 | 73.32 | 0.67 | 91.07 |
| Peru | 2 | South America | 25.13 | 29.10 | 12237 | 76.74 | 0.75 | 86.31 |
| Slovakia | 2 | Europe | 113.13 | 41.20 | 30155 | 77.54 | 0.86 | - |
| Cyprus | 2 | Europe | 127.66 | 37.30 | 32415 | 80.98 | 0.87 | 89.29 |
| Iran | 2 | Asia | 49.83 | 32.40 | 19083 | 76.68 | 0.80 | 63.99 |
| Cambodia | 2 | Asia | 90.67 | 25.60 | 3645 | 69.82 | 0.58 | 51.79 |
| Kuwait | 2 | Asia | 232.13 | 33.70 | 65531 | 75.49 | 0.80 | 86.90 |
| Portugal | 2 | Europe | 112.37 | 46.20 | 27937 | 82.05 | 0.85 | 81.55 |
| United Arab Emirates | 2 | Asia | 112.44 | 34.00 | 67293 | 77.97 | 0.86 | 86.31 |
| Djibouti | 2 | Africa | 41.29 | 25.40 | 2705 | 67.11 | 0.48 | 82.14 |
| Equatorial Guinea | 2 | Africa | 45.19 | 22.40 | 22605 | 58.74 | 0.59 | - |
| Israel | 2 | Asia | 402.61 | 30.60 | 33132 | 82.97 | 0.90 | 89.88 |
| South Korea | 2 | Asia | 527.97 | 43.40 | 35938 | 83.03 | 0.90 | 76.19 |
| Maldives | 2 | Asia | 1454.43 | 30.60 | 15184 | 78.92 | 0.72 | - |
| Bahrain | 2 | Asia | 1935.91 | 32.40 | 43291 | 77.29 | 0.85 | 80.36 |
| United Kingdom | 2 | Europe | 272.90 | 40.80 | 39753 | 81.32 | 0.92 | 74.70 |
| Philippines | 2 | Asia | 351.87 | 25.20 | 7599 | 71.23 | 0.70 | 85.12 |
| Moldova | 2 | Europe | 123.66 | 37.60 | 5190 | 71.90 | 0.70 | 75.00 |
| Macedonia | 2 | Europe | 82.60 | 39.10 | 13111 | 75.80 | 0.76 | - |
| Finland | 2 | Europe | 18.14 | 42.80 | 40586 | 81.91 | 0.92 | 56.55 |
| Malta | 2 | Europe | 1454.04 | 42.40 | 36513 | 82.53 | 0.88 | - |
| Malaysia | 2 | Asia | 96.25 | 29.90 | 26808 | 76.16 | 0.80 | 81.85 |
| Cuba | 2 | North America | 110.41 | 43.10 | 0 | 78.80 | 0.78 | 82.14 |
| Mali | 2 | Africa | 15.20 | 16.40 | 2014 | 59.31 | 0.43 | 66.67 |
| Oman | 2 | Asia | 14.98 | 30.70 | 37961 | 77.86 | 0.82 | 88.69 |
| Saudi Arabia | 2 | Asia | 15.32 | 31.90 | 49045 | 75.13 | 0.85 | 87.50 |
| France | 2 | Europe | 122.58 | 42.00 | 38606 | 82.66 | 0.90 | 79.17 |
| Iraq | 2 | Asia | 88.13 | 20.00 | 15664 | 70.60 | 0.69 | 82.14 |
| Norway | 2 | Europe | 14.46 | 39.70 | 64800 | 82.40 | 0.95 | 69.64 |
| Senegal | 2 | Africa | 82.33 | 18.70 | 2471 | 67.94 | 0.51 | 70.83 |
| Turkey | 2 | Asia | 104.91 | 31.60 | 25129 | 77.69 | 0.79 | 78.87 |
| Trinidad and Tobago | 2 | North America | 266.89 | 36.20 | 28763 | 73.51 | 0.78 | 82.74 |
| Georgia | 2 | Asia | 65.03 | 38.70 | 9745 | 73.77 | 0.78 | 85.12 |
| Taiwan | 2 | Asia | 0.00 | 42.20 | - | 80.46 | - | 44.64 |
| Ukraine | 2 | Europe | 77.39 | 41.40 | 7894 | 72.06 | 0.75 | 79.76 |
| Venezuela | 2 | South America | 36.25 | 29.00 | 16745 | 72.06 | 0.76 | 77.98 |
| D. R. of Congo | 2 | Africa | 35.88 | 17.00 | 808 | 60.68 | 0.46 | 71.13 |
| Czech Republic | 2 | Europe | 137.18 | 43.30 | 32606 | 79.38 | 0.89 | 84.52 |
| Indonesia | 2 | Asia | 145.73 | 29.30 | 11189 | 71.72 | 0.69 | 68.15 |
| Sweden | 2 | Europe | 24.72 | 41.00 | 46949 | 82.80 | 0.93 | 55.36 |
| South Africa | 2 | Africa | 46.75 | 27.30 | 12295 | 64.13 | 0.70 | 84.52 |
| Cameroon | 3 | Africa | 50.89 | 18.80 | 3365 | 59.29 | 0.56 | 59.52 |
| Ethiopia | 3 | Africa | 104.96 | 19.80 | 1730 | 66.60 | 0.46 | 70.24 |
| Latvia | 3 | Europe | 31.21 | 43.90 | 25064 | 75.29 | 0.85 | 64.88 |
| Montenegro | 3 | Europe | 46.28 | 39.10 | 16409 | 76.88 | 0.81 | - |
| Pakistan | 3 | Asia | 255.57 | 23.50 | 5035 | 67.27 | 0.56 | 77.38 |
| Panama | 3 | North America | 55.13 | 29.70 | 22267 | 78.51 | 0.79 | 85.71 |
| Tunisia | 3 | Africa | 74.23 | 32.70 | 10849 | 76.70 | 0.74 | 76.19 |
| Uzbekistan | 3 | Asia | 76.13 | 28.20 | 6253 | 71.72 | 0.71 | 83.04 |
| Kosovo | 3 | Europe | 168.16 | - | 9796 | 71.95 | - | 79.76 |
Table 7.
Countries' data. Source: own construction based on OWID data. Reference: (7): Max containment and health index; (8): Max economic index; (9): Testing policy; (10): Contact tracing; (11): Access health and quality; (12): Total deaths per million at December 1st, 2020
| Country | Cluster | Continent | (7) | (8) | (9) | (10) | (11) | (12) |
| Uruguay | 1 | South America | 61.11 | 87.50 | 3 | 1 | 72.00 | 22.45 |
| Azerbaijan | 1 | Asia | 92.36 | 50.00 | 2 | 2 | 64.50 | 141.33 |
| Burkina Faso | 1 | Africa | 84.72 | - | 1 | 2 | 42.90 | 3.25 |
| Belgium | 1 | Europe | 75.00 | 100.00 | 2 | 2 | 87.90 | 1448.37 |
| Bosnia&Herzegovina | 1 | Europe | 81.25 | 37.50 | 2 | 0 | 78.20 | 831.20 |
| Bolivia | 1 | South America | 81.25 | 50.00 | 1 | 1 | 59.20 | 767.84 |
| Bangladesh | 1 | Asia | 88.19 | 75.00 | 2 | 1 | 51.70 | 40.53 |
| China | 1 | Asia | 85.42 | 62.50 | 3 | 2 | 74.20 | 3.30 |
| Switzerland | 1 | Europe | 61.81 | 62.50 | 2 | 2 | 91.80 | 570.79 |
| Germany | 1 | Europe | 74.31 | 62.50 | 3 | 2 | 86.40 | 205.02 |
| Dominica | 1 | North America | 82.64 | 75.00 | 3 | 2 | 58.10 | 0.00 |
| Dominican Republic | 1 | North America | 90.97 | 25.00 | 2 | 1 | 62.50 | 215.07 |
| Andorra | 1 | Europe | 67.36 | 100.00 | 2 | 1 | 94.60 | 983.63 |
| Algeria | 1 | Africa | 83.33 | 62.50 | 1 | 0 | 63.70 | 55.80 |
| Ghana | 1 | Africa | 81.25 | 50.00 | 3 | 2 | 49.70 | 10.40 |
| Spain | 1 | Europe | 76.39 | 87.50 | 2 | 1 | 89.60 | 973.40 |
| Greece | 1 | Europe | 84.03 | 87.50 | 3 | 2 | 87.00 | 241.48 |
| Guatemala | 1 | North America | 84.03 | 75.00 | 2 | 0 | 55.70 | 233.21 |
| India | 1 | Asia | 98.96 | 75.00 | 2 | 2 | 44.80 | 99.73 |
| Japan | 1 | Asia | 52.08 | 75.00 | 2 | 1 | 89.00 | 16.68 |
| Kyrgyzstan | 1 | Asia | - | - | 1 | 1 | 60.40 | 195.43 |
| Canada | 1 | North America | 72.22 | 75.00 | 3 | 1 | 87.60 | 324.01 |
| Kenya | 1 | Africa | 87.50 | 50.00 | 3 | 1 | 48.70 | 27.41 |
| Armenia | 1 | Asia | - | - | - | - | 67.50 | 740.07 |
| Barbados | 1 | North America | 84.72 | 62.50 | 2 | 2 | 66.80 | 24.36 |
| Kazakhstan | 1 | Asia | 85.76 | 37.50 | 3 | 2 | 61.10 | 129.63 |
| Slovenia | 1 | Europe | 85.42 | 75.00 | 2 | 2 | 87.40 | 716.71 |
| United States | 1 | North America | 76.39 | 62.50 | 3 | 1 | 81.30 | 817.64 |
| Liechtenstein | 1 | Europe | - | - | - | - | - | 419.54 |
| Madagascar | 1 | Africa | 78.47 | 50.00 | 1 | 1 | 43.70 | 9.06 |
| Bulgaria | 1 | Europe | 72.22 | 87.50 | 1 | 2 | 71.40 | 602.73 |
| Jamaica | 1 | North America | 78.47 | 50.00 | 1 | 1 | 63.70 | 87.13 |
| Lebanon | 1 | Asia | 81.25 | 25.00 | 2 | 1 | 80.00 | 151.35 |
| Chile | 1 | South America | 83.68 | 100.00 | 2 | 2 | 76.00 | 807.17 |
| Mexico | 1 | North America | 76.04 | 75.00 | 1 | 1 | 62.60 | 828.07 |
| Morocco | 1 | Africa | 88.19 | 75.00 | 3 | 1 | 61.30 | 160.25 |
| Sri Lanka | 1 | Asia | 88.89 | 50.00 | 2 | 2 | 72.80 | 5.70 |
| Mongolia | 1 | Asia | 84.03 | 75.00 | 2 | 2 | 58.50 | 0.00 |
| Nigeria | 1 | Africa | 80.56 | 62.50 | 1 | 2 | 51.30 | 5.71 |
| Colombia | 1 | South America | 90.28 | 75.00 | 2 | 2 | 67.80 | 725.86 |
| Netherlands | 1 | Europe | 67.36 | 62.50 | 2 | 2 | 89.50 | 555.48 |
| New Zealand | 1 | Oceania | 86.11 | 87.50 | 2 | 2 | 86.20 | 5.18 |
| Afghanistan | 1 | Asia | 76.39 | 25.00 | 1 | 1 | 32.50 | 46.16 |
| Poland | 1 | Europe | 73.61 | 75.00 | 2 | 2 | 79.60 | 465.01 |
| Paraguay | 1 | South America | 80.56 | 75.00 | 1 | 2 | 60.40 | 248.30 |
| Palestine | 1 | Asia | 79.17 | 50.00 | 1 | 1 | 70.50 | 146.43 |
| Qatar | 1 | Asia | 87.50 | 62.50 | 3 | 2 | 85.20 | 82.61 |
| Romania | 1 | Europe | 79.86 | 87.50 | 2 | 1 | 74.40 | 599.35 |
| Rwanda | 1 | Africa | 90.97 | 62.50 | 3 | 2 | 47.80 | 3.78 |
| Egypt | 1 | Africa | 81.25 | 75.00 | 2 | 2 | 61.00 | 65.14 |
| Russia | 1 | Europe | 80.56 | 62.50 | 3 | 2 | 71.70 | 274.44 |
| Thailand | 1 | Asia | 76.74 | 100.00 | 2 | 2 | 70.80 | 0.86 |
| Togo | 1 | Africa | 72.22 | 75.00 | 1 | 0 | 44.30 | 7.73 |
| Vietnam | 1 | Asia | 93.06 | 50.00 | 3 | 2 | 66.30 | 0.36 |
| Singapore | 1 | Asia | 86.11 | 100.00 | 2 | 2 | 86.30 | 4.96 |
| Zambia | 1 | Africa | 70.49 | 25.00 | 1 | 2 | 41.60 | 19.42 |
| Albania | 2 | Europe | 77.08 | 75.00 | 1 | 2 | 78.20 | 285.64 |
| Australia | 2 | Oceania | 83.33 | 75.00 | 3 | 2 | 89.80 | 35.61 |
| Austria | 2 | Europe | 84.72 | 100.00 | 3 | 2 | 88.20 | 369.18 |
| Brunei | 2 | Asia | 55.56 | 50.00 | 2 | 1 | 70.00 | 6.86 |
| Brazil | 2 | South America | 81.94 | 50.00 | 2 | 2 | 64.90 | 817.73 |
| Cote d'Ivoire | 2 | Africa | 75.35 | 75.00 | 2 | 2 | 42.40 | 5.00 |
| Argentina | 2 | South America | 92.36 | 75.00 | 1 | 2 | 68.40 | 861.32 |
| Denmark | 2 | Europe | 65.97 | 100.00 | 3 | 2 | 85.70 | 146.06 |
| Belarus | 2 | Europe | 35.42 | - | 3 | 2 | 74.40 | 123.40 |
| Estonia | 2 | Europe | 63.89 | 87.50 | 2 | 0 | 81.40 | 91.22 |
| Costa Rica | 2 | North America | 72.22 | 50.00 | 1 | 2 | 72.90 | 339.80 |
| Honduras | 2 | North America | 88.19 | 87.50 | 1 | 1 | 53.90 | 294.61 |
| Serbia | 2 | Europe | 86.11 | 62.50 | 1 | 2 | 75.40 | 242.78 |
| Hungary | 2 | Europe | 72.22 | 87.50 | 1 | 2 | 79.60 | 515.20 |
| Ireland | 2 | Europe | 79.17 | 100.00 | 2 | 2 | 88.40 | 419.01 |
| Croatia | 2 | Europe | 86.11 | 87.50 | 3 | 2 | 81.60 | 453.32 |
| Iceland | 2 | Europe | 60.42 | 100.00 | 3 | 2 | 93.60 | 79.12 |
| Italy | 2 | Europe | 91.32 | 75.00 | 2 | 2 | 88.70 | 932.18 |
| El Salvador | 2 | North America | 93.75 | 75.00 | 3 | 1 | 64.40 | 172.67 |
| Peru | 2 | South America | 88.19 | 75.00 | 2 | 2 | 69.60 | 1090.81 |
| Slovakia | 2 | Europe | - | - | 1 | 2 | 78.60 | 158.99 |
| Cyprus | 2 | Europe | 87.50 | 100.00 | 3 | 2 | 85.30 | 55.94 |
| Iran | 2 | Asia | 68.06 | 62.50 | 2 | 0 | 71.10 | 578.95 |
| Cambodia | 2 | Asia | 60.42 | 62.50 | 1 | 1 | 50.70 | 0.00 |
| Kuwait | 2 | Asia | 95.14 | 37.50 | 2 | 2 | 82.00 | 206.30 |
| Portugal | 2 | Europe | 82.64 | 75.00 | 3 | 1 | 84.50 | 448.87 |
| United Arab Emirates | 2 | Asia | 92.36 | 50.00 | 3 | 2 | 72.20 | 58.24 |
| Djibouti | 2 | Africa | 93.75 | 12.50 | 2 | 2 | 44.70 | 61.74 |
| Equatorial Guinea | 2 | Africa | - | - | - | - | 48.40 | 60.59 |
| Israel | 2 | Asia | 88.19 | 100.00 | 1 | 2 | 85.50 | 332.39 |
| South Korea | 2 | Asia | 80.56 | 50.00 | 3 | 2 | 85.80 | 10.26 |
| Maldives | 2 | Asia | - | - | - | - | 75.50 | 86.95 |
| Bahrain | 2 | Asia | 79.17 | 87.50 | 3 | 2 | 79.00 | 200.40 |
| United Kingdom | 2 | Europe | 70.49 | 100.00 | 2 | 2 | 84.60 | 871.28 |
| Philippines | 2 | Asia | 88.89 | 75.00 | 2 | 2 | 52.00 | 76.82 |
| Moldova | 2 | Europe | 81.25 | 37.50 | 2 | 2 | 73.10 | 575.86 |
| Macedonia | 2 | Europe | - | - | - | - | 76.00 | 860.14 |
| Finland | 2 | Europe | 57.64 | 75.00 | 2 | 1 | 89.60 | 72.01 |
| Malta | 2 | Europe | - | - | - | - | 85.10 | 319.34 |
| Malaysia | 2 | Asia | 82.99 | 75.00 | 3 | 2 | 66.60 | 11.22 |
| Cuba | 2 | North America | 87.50 | 62.50 | 1 | 2 | 73.50 | 12.01 |
| Mali | 2 | Africa | 69.44 | 50.00 | 1 | 2 | 45.60 | 7.90 |
| Oman | 2 | Asia | 93.06 | 62.50 | 2 | 1 | 77.10 | 280.03 |
| Saudi Arabia | 2 | Asia | 91.67 | 62.50 | 3 | 2 | 79.40 | 169.67 |
| France | 2 | Europe | 82.99 | 100.00 | 3 | 2 | 87.90 | 809.23 |
| Iraq | 2 | Asia | 90.28 | 50.00 | 2 | 1 | 60.10 | 305.95 |
| Norway | 2 | Europe | 66.67 | 87.50 | 2 | 2 | 90.50 | 61.61 |
| Senegal | 2 | Africa | 72.22 | 75.00 | 2 | 2 | 44.40 | 19.89 |
| Turkey | 2 | Asia | 77.43 | 87.50 | 2 | 2 | 76.20 | 165.24 |
| Trinidad and Tobago | 2 | North America | 84.03 | 75.00 | 1 | 2 | 62.10 | 85.75 |
| Georgia | 2 | Asia | 93.06 | 37.50 | 2 | 2 | 62.10 | 326.63 |
| Taiwan | 2 | Asia | 45.83 | 37.50 | 3 | 2 | 77.60 | 0.29 |
| Ukraine | 2 | Europe | 86.81 | 37.50 | 2 | 2 | 72.70 | 296.38 |
| Venezuela | 2 | South America | 82.64 | 50.00 | 3 | 0 | 64.70 | 31.69 |
| D. R. of Congo | 2 | Africa | 74.65 | 50.00 | 1 | 1 | 40.40 | 3.74 |
| Czech Republic | 2 | Europe | 81.94 | 100.00 | 2 | 2 | 84.80 | 785.04 |
| Indonesia | 2 | Asia | 75.35 | 37.50 | 1 | 1 | 49.20 | 62.45 |
| Sweden | 2 | Europe | 58.33 | 62.50 | 2 | 1 | 90.50 | 673.12 |
| South Africa | 2 | Africa | 86.11 | 75.00 | 3 | 2 | 52.00 | 364.94 |
| Cameroon | 3 | Africa | 69.44 | 37.50 | 2 | 1 | 44.40 | 16.61 |
| Ethiopia | 3 | Africa | 73.61 | 50.00 | 1 | 1 | 44.20 | 14.87 |
| Latvia | 3 | Europe | 60.07 | 100.00 | 1 | 2 | 77.70 | 111.34 |
| Montenegro | 3 | Europe | - | - | - | - | 80.70 | 802.47 |
| Pakistan | 3 | Asia | 81.94 | 75.00 | 2 | 1 | 43.10 | 36.97 |
| Panama | 3 | North America | 87.50 | 75.00 | 2 | 1 | 64.40 | 718.00 |
| Tunisia | 3 | Africa | 76.39 | 75.00 | 1 | 1 | 70.10 | 275.84 |
| Uzbekistan | 3 | Asia | 88.54 | 50.00 | 1 | 2 | 62.30 | 18.26 |
| Kosovo | 3 | Europe | 76.39 | 100.00 | 1 | 1 | - | 530.84 |
| Country | Cluster | Continent | (7) | (8) | (9) | (10) | (11) | (12) |
| Uruguay | 1 | South America | 61.11 | 87.50 | 3 | 1 | 72.00 | 22.45 |
| Azerbaijan | 1 | Asia | 92.36 | 50.00 | 2 | 2 | 64.50 | 141.33 |
| Burkina Faso | 1 | Africa | 84.72 | - | 1 | 2 | 42.90 | 3.25 |
| Belgium | 1 | Europe | 75.00 | 100.00 | 2 | 2 | 87.90 | 1448.37 |
| Bosnia&Herzegovina | 1 | Europe | 81.25 | 37.50 | 2 | 0 | 78.20 | 831.20 |
| Bolivia | 1 | South America | 81.25 | 50.00 | 1 | 1 | 59.20 | 767.84 |
| Bangladesh | 1 | Asia | 88.19 | 75.00 | 2 | 1 | 51.70 | 40.53 |
| China | 1 | Asia | 85.42 | 62.50 | 3 | 2 | 74.20 | 3.30 |
| Switzerland | 1 | Europe | 61.81 | 62.50 | 2 | 2 | 91.80 | 570.79 |
| Germany | 1 | Europe | 74.31 | 62.50 | 3 | 2 | 86.40 | 205.02 |
| Dominica | 1 | North America | 82.64 | 75.00 | 3 | 2 | 58.10 | 0.00 |
| Dominican Republic | 1 | North America | 90.97 | 25.00 | 2 | 1 | 62.50 | 215.07 |
| Andorra | 1 | Europe | 67.36 | 100.00 | 2 | 1 | 94.60 | 983.63 |
| Algeria | 1 | Africa | 83.33 | 62.50 | 1 | 0 | 63.70 | 55.80 |
| Ghana | 1 | Africa | 81.25 | 50.00 | 3 | 2 | 49.70 | 10.40 |
| Spain | 1 | Europe | 76.39 | 87.50 | 2 | 1 | 89.60 | 973.40 |
| Greece | 1 | Europe | 84.03 | 87.50 | 3 | 2 | 87.00 | 241.48 |
| Guatemala | 1 | North America | 84.03 | 75.00 | 2 | 0 | 55.70 | 233.21 |
| India | 1 | Asia | 98.96 | 75.00 | 2 | 2 | 44.80 | 99.73 |
| Japan | 1 | Asia | 52.08 | 75.00 | 2 | 1 | 89.00 | 16.68 |
| Kyrgyzstan | 1 | Asia | - | - | 1 | 1 | 60.40 | 195.43 |
| Canada | 1 | North America | 72.22 | 75.00 | 3 | 1 | 87.60 | 324.01 |
| Kenya | 1 | Africa | 87.50 | 50.00 | 3 | 1 | 48.70 | 27.41 |
| Armenia | 1 | Asia | - | - | - | - | 67.50 | 740.07 |
| Barbados | 1 | North America | 84.72 | 62.50 | 2 | 2 | 66.80 | 24.36 |
| Kazakhstan | 1 | Asia | 85.76 | 37.50 | 3 | 2 | 61.10 | 129.63 |
| Slovenia | 1 | Europe | 85.42 | 75.00 | 2 | 2 | 87.40 | 716.71 |
| United States | 1 | North America | 76.39 | 62.50 | 3 | 1 | 81.30 | 817.64 |
| Liechtenstein | 1 | Europe | - | - | - | - | - | 419.54 |
| Madagascar | 1 | Africa | 78.47 | 50.00 | 1 | 1 | 43.70 | 9.06 |
| Bulgaria | 1 | Europe | 72.22 | 87.50 | 1 | 2 | 71.40 | 602.73 |
| Jamaica | 1 | North America | 78.47 | 50.00 | 1 | 1 | 63.70 | 87.13 |
| Lebanon | 1 | Asia | 81.25 | 25.00 | 2 | 1 | 80.00 | 151.35 |
| Chile | 1 | South America | 83.68 | 100.00 | 2 | 2 | 76.00 | 807.17 |
| Mexico | 1 | North America | 76.04 | 75.00 | 1 | 1 | 62.60 | 828.07 |
| Morocco | 1 | Africa | 88.19 | 75.00 | 3 | 1 | 61.30 | 160.25 |
| Sri Lanka | 1 | Asia | 88.89 | 50.00 | 2 | 2 | 72.80 | 5.70 |
| Mongolia | 1 | Asia | 84.03 | 75.00 | 2 | 2 | 58.50 | 0.00 |
| Nigeria | 1 | Africa | 80.56 | 62.50 | 1 | 2 | 51.30 | 5.71 |
| Colombia | 1 | South America | 90.28 | 75.00 | 2 | 2 | 67.80 | 725.86 |
| Netherlands | 1 | Europe | 67.36 | 62.50 | 2 | 2 | 89.50 | 555.48 |
| New Zealand | 1 | Oceania | 86.11 | 87.50 | 2 | 2 | 86.20 | 5.18 |
| Afghanistan | 1 | Asia | 76.39 | 25.00 | 1 | 1 | 32.50 | 46.16 |
| Poland | 1 | Europe | 73.61 | 75.00 | 2 | 2 | 79.60 | 465.01 |
| Paraguay | 1 | South America | 80.56 | 75.00 | 1 | 2 | 60.40 | 248.30 |
| Palestine | 1 | Asia | 79.17 | 50.00 | 1 | 1 | 70.50 | 146.43 |
| Qatar | 1 | Asia | 87.50 | 62.50 | 3 | 2 | 85.20 | 82.61 |
| Romania | 1 | Europe | 79.86 | 87.50 | 2 | 1 | 74.40 | 599.35 |
| Rwanda | 1 | Africa | 90.97 | 62.50 | 3 | 2 | 47.80 | 3.78 |
| Egypt | 1 | Africa | 81.25 | 75.00 | 2 | 2 | 61.00 | 65.14 |
| Russia | 1 | Europe | 80.56 | 62.50 | 3 | 2 | 71.70 | 274.44 |
| Thailand | 1 | Asia | 76.74 | 100.00 | 2 | 2 | 70.80 | 0.86 |
| Togo | 1 | Africa | 72.22 | 75.00 | 1 | 0 | 44.30 | 7.73 |
| Vietnam | 1 | Asia | 93.06 | 50.00 | 3 | 2 | 66.30 | 0.36 |
| Singapore | 1 | Asia | 86.11 | 100.00 | 2 | 2 | 86.30 | 4.96 |
| Zambia | 1 | Africa | 70.49 | 25.00 | 1 | 2 | 41.60 | 19.42 |
| Albania | 2 | Europe | 77.08 | 75.00 | 1 | 2 | 78.20 | 285.64 |
| Australia | 2 | Oceania | 83.33 | 75.00 | 3 | 2 | 89.80 | 35.61 |
| Austria | 2 | Europe | 84.72 | 100.00 | 3 | 2 | 88.20 | 369.18 |
| Brunei | 2 | Asia | 55.56 | 50.00 | 2 | 1 | 70.00 | 6.86 |
| Brazil | 2 | South America | 81.94 | 50.00 | 2 | 2 | 64.90 | 817.73 |
| Cote d'Ivoire | 2 | Africa | 75.35 | 75.00 | 2 | 2 | 42.40 | 5.00 |
| Argentina | 2 | South America | 92.36 | 75.00 | 1 | 2 | 68.40 | 861.32 |
| Denmark | 2 | Europe | 65.97 | 100.00 | 3 | 2 | 85.70 | 146.06 |
| Belarus | 2 | Europe | 35.42 | - | 3 | 2 | 74.40 | 123.40 |
| Estonia | 2 | Europe | 63.89 | 87.50 | 2 | 0 | 81.40 | 91.22 |
| Costa Rica | 2 | North America | 72.22 | 50.00 | 1 | 2 | 72.90 | 339.80 |
| Honduras | 2 | North America | 88.19 | 87.50 | 1 | 1 | 53.90 | 294.61 |
| Serbia | 2 | Europe | 86.11 | 62.50 | 1 | 2 | 75.40 | 242.78 |
| Hungary | 2 | Europe | 72.22 | 87.50 | 1 | 2 | 79.60 | 515.20 |
| Ireland | 2 | Europe | 79.17 | 100.00 | 2 | 2 | 88.40 | 419.01 |
| Croatia | 2 | Europe | 86.11 | 87.50 | 3 | 2 | 81.60 | 453.32 |
| Iceland | 2 | Europe | 60.42 | 100.00 | 3 | 2 | 93.60 | 79.12 |
| Italy | 2 | Europe | 91.32 | 75.00 | 2 | 2 | 88.70 | 932.18 |
| El Salvador | 2 | North America | 93.75 | 75.00 | 3 | 1 | 64.40 | 172.67 |
| Peru | 2 | South America | 88.19 | 75.00 | 2 | 2 | 69.60 | 1090.81 |
| Slovakia | 2 | Europe | - | - | 1 | 2 | 78.60 | 158.99 |
| Cyprus | 2 | Europe | 87.50 | 100.00 | 3 | 2 | 85.30 | 55.94 |
| Iran | 2 | Asia | 68.06 | 62.50 | 2 | 0 | 71.10 | 578.95 |
| Cambodia | 2 | Asia | 60.42 | 62.50 | 1 | 1 | 50.70 | 0.00 |
| Kuwait | 2 | Asia | 95.14 | 37.50 | 2 | 2 | 82.00 | 206.30 |
| Portugal | 2 | Europe | 82.64 | 75.00 | 3 | 1 | 84.50 | 448.87 |
| United Arab Emirates | 2 | Asia | 92.36 | 50.00 | 3 | 2 | 72.20 | 58.24 |
| Djibouti | 2 | Africa | 93.75 | 12.50 | 2 | 2 | 44.70 | 61.74 |
| Equatorial Guinea | 2 | Africa | - | - | - | - | 48.40 | 60.59 |
| Israel | 2 | Asia | 88.19 | 100.00 | 1 | 2 | 85.50 | 332.39 |
| South Korea | 2 | Asia | 80.56 | 50.00 | 3 | 2 | 85.80 | 10.26 |
| Maldives | 2 | Asia | - | - | - | - | 75.50 | 86.95 |
| Bahrain | 2 | Asia | 79.17 | 87.50 | 3 | 2 | 79.00 | 200.40 |
| United Kingdom | 2 | Europe | 70.49 | 100.00 | 2 | 2 | 84.60 | 871.28 |
| Philippines | 2 | Asia | 88.89 | 75.00 | 2 | 2 | 52.00 | 76.82 |
| Moldova | 2 | Europe | 81.25 | 37.50 | 2 | 2 | 73.10 | 575.86 |
| Macedonia | 2 | Europe | - | - | - | - | 76.00 | 860.14 |
| Finland | 2 | Europe | 57.64 | 75.00 | 2 | 1 | 89.60 | 72.01 |
| Malta | 2 | Europe | - | - | - | - | 85.10 | 319.34 |
| Malaysia | 2 | Asia | 82.99 | 75.00 | 3 | 2 | 66.60 | 11.22 |
| Cuba | 2 | North America | 87.50 | 62.50 | 1 | 2 | 73.50 | 12.01 |
| Mali | 2 | Africa | 69.44 | 50.00 | 1 | 2 | 45.60 | 7.90 |
| Oman | 2 | Asia | 93.06 | 62.50 | 2 | 1 | 77.10 | 280.03 |
| Saudi Arabia | 2 | Asia | 91.67 | 62.50 | 3 | 2 | 79.40 | 169.67 |
| France | 2 | Europe | 82.99 | 100.00 | 3 | 2 | 87.90 | 809.23 |
| Iraq | 2 | Asia | 90.28 | 50.00 | 2 | 1 | 60.10 | 305.95 |
| Norway | 2 | Europe | 66.67 | 87.50 | 2 | 2 | 90.50 | 61.61 |
| Senegal | 2 | Africa | 72.22 | 75.00 | 2 | 2 | 44.40 | 19.89 |
| Turkey | 2 | Asia | 77.43 | 87.50 | 2 | 2 | 76.20 | 165.24 |
| Trinidad and Tobago | 2 | North America | 84.03 | 75.00 | 1 | 2 | 62.10 | 85.75 |
| Georgia | 2 | Asia | 93.06 | 37.50 | 2 | 2 | 62.10 | 326.63 |
| Taiwan | 2 | Asia | 45.83 | 37.50 | 3 | 2 | 77.60 | 0.29 |
| Ukraine | 2 | Europe | 86.81 | 37.50 | 2 | 2 | 72.70 | 296.38 |
| Venezuela | 2 | South America | 82.64 | 50.00 | 3 | 0 | 64.70 | 31.69 |
| D. R. of Congo | 2 | Africa | 74.65 | 50.00 | 1 | 1 | 40.40 | 3.74 |
| Czech Republic | 2 | Europe | 81.94 | 100.00 | 2 | 2 | 84.80 | 785.04 |
| Indonesia | 2 | Asia | 75.35 | 37.50 | 1 | 1 | 49.20 | 62.45 |
| Sweden | 2 | Europe | 58.33 | 62.50 | 2 | 1 | 90.50 | 673.12 |
| South Africa | 2 | Africa | 86.11 | 75.00 | 3 | 2 | 52.00 | 364.94 |
| Cameroon | 3 | Africa | 69.44 | 37.50 | 2 | 1 | 44.40 | 16.61 |
| Ethiopia | 3 | Africa | 73.61 | 50.00 | 1 | 1 | 44.20 | 14.87 |
| Latvia | 3 | Europe | 60.07 | 100.00 | 1 | 2 | 77.70 | 111.34 |
| Montenegro | 3 | Europe | - | - | - | - | 80.70 | 802.47 |
| Pakistan | 3 | Asia | 81.94 | 75.00 | 2 | 1 | 43.10 | 36.97 |
| Panama | 3 | North America | 87.50 | 75.00 | 2 | 1 | 64.40 | 718.00 |
| Tunisia | 3 | Africa | 76.39 | 75.00 | 1 | 1 | 70.10 | 275.84 |
| Uzbekistan | 3 | Asia | 88.54 | 50.00 | 1 | 2 | 62.30 | 18.26 |
| Kosovo | 3 | Europe | 76.39 | 100.00 | 1 | 1 | - | 530.84 |
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